Goal-oriented Data Warehouse Quality Measurement

TL;DR

This paper introduces i*DWQM, a modeling framework extending goal-oriented requirements engineering to measure and assure data-warehouse quality through quantifiable scenarios and UML profiling.

Contribution

It presents a novel extension of goal-oriented requirements modeling for data warehouses, enabling quality measurement with a UML-based framework.

Findings

Framework supports defining quantifiable quality scenarios

Implementation available in Eclipse platform

Enhances data-warehouse quality assurance

Abstract

Requirements engineering is known to be a key factor for the success of software projects. Inside this discipline, goal-oriented requirements engineering approaches have shown specially suitable to deal with projects where it is necessary to capture the alignment between system requirements and stakeholders' needs, as is the case of data-warehousing projects. However, the mere alignment of data-warehouse system requirements with business goals is not enough to assure better data-warehousing products; measures and techniques are also needed to assure the data-warehouse quality. In this paper, we provide a modelling framework for data-warehouse quality measurement (i*DWQM). This framework, conceived as an i* extension, provides support for the definition of data-warehouse requirements analysis models that include quantifiable quality scenarios, defined in terms of well-formed measures. This extension has been defined by means of a UML profiling architecture. The resulting framework has been implemented in the Eclipse development platform.